Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory792.8 KiB
Average record size in memory811.9 B

Variable types

Numeric7
Text3
Unsupported2
Categorical2

Alerts

Genre_Count has constant value "1" Constant
Decade is highly overall correlated with YearHigh correlation
Metascore is highly overall correlated with RatingHigh correlation
Rating is highly overall correlated with Metascore and 1 other fieldsHigh correlation
Revenue is highly overall correlated with VotesHigh correlation
Votes is highly overall correlated with Rating and 2 other fieldsHigh correlation
Year is highly overall correlated with Decade and 1 other fieldsHigh correlation
Rank is uniformly distributed Uniform
Rank has unique values Unique
Description has unique values Unique
Genre is an unsupported type, check if it needs cleaning or further analysis Unsupported
Actors is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-05-24 09:59:41.866816
Analysis finished2025-05-24 10:00:00.341484
Duration18.47 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Rank
Real number (ℝ)

Uniform  Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.5
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-05-24T15:30:00.618889image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile950.05
Maximum1000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.81944
Coefficient of variation (CV)0.57706181
Kurtosis-1.2
Mean500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83416.667
MonotonicityStrictly increasing
2025-05-24T15:30:00.951856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
672 1
 
0.1%
659 1
 
0.1%
660 1
 
0.1%
661 1
 
0.1%
662 1
 
0.1%
663 1
 
0.1%
664 1
 
0.1%
665 1
 
0.1%
666 1
 
0.1%
Other values (990) 990
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1000 1
0.1%
999 1
0.1%
998 1
0.1%
997 1
0.1%
996 1
0.1%
995 1
0.1%
994 1
0.1%
993 1
0.1%
992 1
0.1%
991 1
0.1%

Title
Text

Distinct999
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size70.3 KiB
2025-05-24T15:30:01.826288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length61
Median length44
Mean length14.539
Min length2

Characters and Unicode

Total characters14539
Distinct characters81
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique998 ?
Unique (%)99.8%

Sample

1st rowGuardians of the Galaxy
2nd rowPrometheus
3rd rowSplit
4th rowSing
5th rowSuicide Squad
ValueCountFrequency (%)
the 305
 
11.7%
of 92
 
3.5%
a 29
 
1.1%
in 22
 
0.8%
and 22
 
0.8%
2 22
 
0.8%
15
 
0.6%
man 12
 
0.5%
to 12
 
0.5%
i 11
 
0.4%
Other values (1429) 2063
79.2%
2025-05-24T15:30:03.243716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1605
 
11.0%
e 1507
 
10.4%
a 884
 
6.1%
o 851
 
5.9%
n 828
 
5.7%
r 799
 
5.5%
i 775
 
5.3%
t 720
 
5.0%
s 609
 
4.2%
h 539
 
3.7%
Other values (71) 5422
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10340
71.1%
Uppercase Letter 2274
 
15.6%
Space Separator 1605
 
11.0%
Other Punctuation 171
 
1.2%
Decimal Number 110
 
0.8%
Dash Punctuation 31
 
0.2%
Close Punctuation 4
 
< 0.1%
Open Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1507
14.6%
a 884
 
8.5%
o 851
 
8.2%
n 828
 
8.0%
r 799
 
7.7%
i 775
 
7.5%
t 720
 
7.0%
s 609
 
5.9%
h 539
 
5.2%
l 457
 
4.4%
Other values (22) 2371
22.9%
Uppercase Letter
ValueCountFrequency (%)
T 350
15.4%
S 188
 
8.3%
M 141
 
6.2%
B 133
 
5.8%
D 125
 
5.5%
A 115
 
5.1%
P 110
 
4.8%
H 105
 
4.6%
C 104
 
4.6%
W 100
 
4.4%
Other values (16) 803
35.3%
Decimal Number
ValueCountFrequency (%)
2 35
31.8%
3 17
15.5%
0 15
13.6%
1 15
13.6%
5 7
 
6.4%
4 7
 
6.4%
7 5
 
4.5%
6 3
 
2.7%
8 3
 
2.7%
9 3
 
2.7%
Other Punctuation
ValueCountFrequency (%)
: 85
49.7%
' 39
22.8%
. 23
 
13.5%
, 9
 
5.3%
& 6
 
3.5%
! 4
 
2.3%
/ 2
 
1.2%
? 2
 
1.2%
· 1
 
0.6%
Space Separator
ValueCountFrequency (%)
1605
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12614
86.8%
Common 1925
 
13.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1507
 
11.9%
a 884
 
7.0%
o 851
 
6.7%
n 828
 
6.6%
r 799
 
6.3%
i 775
 
6.1%
t 720
 
5.7%
s 609
 
4.8%
h 539
 
4.3%
l 457
 
3.6%
Other values (48) 4645
36.8%
Common
ValueCountFrequency (%)
1605
83.4%
: 85
 
4.4%
' 39
 
2.0%
2 35
 
1.8%
- 31
 
1.6%
. 23
 
1.2%
3 17
 
0.9%
0 15
 
0.8%
1 15
 
0.8%
, 9
 
0.5%
Other values (13) 51
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14530
99.9%
None 9
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1605
 
11.0%
e 1507
 
10.4%
a 884
 
6.1%
o 851
 
5.9%
n 828
 
5.7%
r 799
 
5.5%
i 775
 
5.3%
t 720
 
5.0%
s 609
 
4.2%
h 539
 
3.7%
Other values (64) 5413
37.3%
None
ValueCountFrequency (%)
é 3
33.3%
ä 1
 
11.1%
í 1
 
11.1%
á 1
 
11.1%
ç 1
 
11.1%
· 1
 
11.1%
è 1
 
11.1%

Genre
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size156.4 KiB

Description
Text

Unique 

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size221.2 KiB
2025-05-24T15:30:03.977715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length421
Median length212
Mean length163.232
Min length42

Characters and Unicode

Total characters163232
Distinct characters82
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowA group of intergalactic criminals are forced to work together to stop a fanatical warrior from taking control of the universe.
2nd rowFollowing clues to the origin of mankind, a team finds a structure on a distant moon, but they soon realize they are not alone.
3rd rowThree girls are kidnapped by a man with a diagnosed 23 distinct personalities. They must try to escape before the apparent emergence of a frightful new 24th.
4th rowIn a city of humanoid animals, a hustling theater impresario's attempt to save his theater with a singing competition becomes grander than he anticipates even as its finalists' find that their lives will never be the same.
5th rowA secret government agency recruits some of the most dangerous incarcerated super-villains to form a defensive task force. Their first mission: save the world from the apocalypse.
ValueCountFrequency (%)
a 1626
 
5.8%
the 1360
 
4.9%
to 934
 
3.3%
of 807
 
2.9%
and 716
 
2.6%
in 578
 
2.1%
his 487
 
1.7%
an 304
 
1.1%
is 296
 
1.1%
with 274
 
1.0%
Other values (6172) 20539
73.6%
2025-05-24T15:30:05.215071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26921
16.5%
e 15840
 
9.7%
t 10926
 
6.7%
a 10686
 
6.5%
i 9657
 
5.9%
o 9618
 
5.9%
n 9602
 
5.9%
r 9227
 
5.7%
s 8727
 
5.3%
h 6513
 
4.0%
Other values (72) 45515
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 128516
78.7%
Space Separator 26921
 
16.5%
Uppercase Letter 3786
 
2.3%
Other Punctuation 2995
 
1.8%
Decimal Number 506
 
0.3%
Dash Punctuation 438
 
0.3%
Close Punctuation 24
 
< 0.1%
Open Punctuation 24
 
< 0.1%
Final Punctuation 20
 
< 0.1%
Currency Symbol 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 15840
12.3%
t 10926
 
8.5%
a 10686
 
8.3%
i 9657
 
7.5%
o 9618
 
7.5%
n 9602
 
7.5%
r 9227
 
7.2%
s 8727
 
6.8%
h 6513
 
5.1%
l 5169
 
4.0%
Other values (20) 32551
25.3%
Uppercase Letter
ValueCountFrequency (%)
A 688
18.2%
T 290
 
7.7%
S 271
 
7.2%
B 227
 
6.0%
W 211
 
5.6%
C 204
 
5.4%
I 201
 
5.3%
M 192
 
5.1%
F 142
 
3.8%
H 140
 
3.7%
Other values (16) 1220
32.2%
Other Punctuation
ValueCountFrequency (%)
. 1365
45.6%
, 1216
40.6%
' 297
 
9.9%
" 66
 
2.2%
: 26
 
0.9%
? 11
 
0.4%
; 8
 
0.3%
/ 4
 
0.1%
! 1
 
< 0.1%
# 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 110
21.7%
0 108
21.3%
9 92
18.2%
2 53
10.5%
7 31
 
6.1%
8 28
 
5.5%
6 25
 
4.9%
5 23
 
4.5%
4 23
 
4.5%
3 13
 
2.6%
Space Separator
ValueCountFrequency (%)
26921
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 438
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Final Punctuation
ValueCountFrequency (%)
» 20
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 132302
81.1%
Common 30930
 
18.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 15840
12.0%
t 10926
 
8.3%
a 10686
 
8.1%
i 9657
 
7.3%
o 9618
 
7.3%
n 9602
 
7.3%
r 9227
 
7.0%
s 8727
 
6.6%
h 6513
 
4.9%
l 5169
 
3.9%
Other values (46) 36337
27.5%
Common
ValueCountFrequency (%)
26921
87.0%
. 1365
 
4.4%
, 1216
 
3.9%
- 438
 
1.4%
' 297
 
1.0%
1 110
 
0.4%
0 108
 
0.3%
9 92
 
0.3%
" 66
 
0.2%
2 53
 
0.2%
Other values (16) 264
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 163203
> 99.9%
None 29
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26921
16.5%
e 15840
 
9.7%
t 10926
 
6.7%
a 10686
 
6.5%
i 9657
 
5.9%
o 9618
 
5.9%
n 9602
 
5.9%
r 9227
 
5.7%
s 8727
 
5.3%
h 6513
 
4.0%
Other values (67) 45486
27.9%
None
ValueCountFrequency (%)
» 20
69.0%
é 4
 
13.8%
á 2
 
6.9%
è 2
 
6.9%
í 1
 
3.4%

Director
Text

Distinct644
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Memory size70.0 KiB
2025-05-24T15:30:06.080425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length21
Mean length13.139
Min length3

Characters and Unicode

Total characters13139
Distinct characters69
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique444 ?
Unique (%)44.4%

Sample

1st rowJames Gunn
2nd rowRidley Scott
3rd rowM. Night Shyamalan
4th rowChristophe Lourdelet
5th rowDavid Ayer
ValueCountFrequency (%)
david 38
 
1.8%
john 25
 
1.2%
michael 22
 
1.1%
james 21
 
1.0%
scott 20
 
1.0%
paul 19
 
0.9%
robert 14
 
0.7%
steven 13
 
0.6%
lee 12
 
0.6%
peter 12
 
0.6%
Other values (977) 1896
90.6%
2025-05-24T15:30:07.251798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1223
 
9.3%
1092
 
8.3%
a 1056
 
8.0%
n 937
 
7.1%
r 875
 
6.7%
o 783
 
6.0%
i 740
 
5.6%
l 604
 
4.6%
t 486
 
3.7%
s 467
 
3.6%
Other values (59) 4876
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9802
74.6%
Uppercase Letter 2153
 
16.4%
Space Separator 1092
 
8.3%
Other Punctuation 73
 
0.6%
Dash Punctuation 19
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1223
12.5%
a 1056
10.8%
n 937
9.6%
r 875
 
8.9%
o 783
 
8.0%
i 740
 
7.5%
l 604
 
6.2%
t 486
 
5.0%
s 467
 
4.8%
h 357
 
3.6%
Other values (28) 2274
23.2%
Uppercase Letter
ValueCountFrequency (%)
S 207
 
9.6%
J 200
 
9.3%
M 183
 
8.5%
A 148
 
6.9%
D 137
 
6.4%
G 131
 
6.1%
B 127
 
5.9%
C 123
 
5.7%
R 119
 
5.5%
L 108
 
5.0%
Other values (17) 670
31.1%
Other Punctuation
ValueCountFrequency (%)
. 71
97.3%
' 2
 
2.7%
Space Separator
ValueCountFrequency (%)
1092
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11955
91.0%
Common 1184
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1223
 
10.2%
a 1056
 
8.8%
n 937
 
7.8%
r 875
 
7.3%
o 783
 
6.5%
i 740
 
6.2%
l 604
 
5.1%
t 486
 
4.1%
s 467
 
3.9%
h 357
 
3.0%
Other values (55) 4427
37.0%
Common
ValueCountFrequency (%)
1092
92.2%
. 71
 
6.0%
- 19
 
1.6%
' 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13095
99.7%
None 44
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1223
 
9.3%
1092
 
8.3%
a 1056
 
8.1%
n 937
 
7.2%
r 875
 
6.7%
o 783
 
6.0%
i 740
 
5.7%
l 604
 
4.6%
t 486
 
3.7%
s 467
 
3.6%
Other values (46) 4832
36.9%
None
ValueCountFrequency (%)
é 11
25.0%
á 9
20.5%
ó 4
 
9.1%
ö 4
 
9.1%
å 4
 
9.1%
ç 3
 
6.8%
ñ 3
 
6.8%
ø 1
 
2.3%
ë 1
 
2.3%
û 1
 
2.3%
Other values (3) 3
 
6.8%

Actors
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size156.4 KiB

Year
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.783
Minimum2006
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-05-24T15:30:07.563003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2007
Q12010
median2014
Q32016
95-th percentile2016
Maximum2016
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2059615
Coefficient of variation (CV)0.0015928004
Kurtosis-0.82196398
Mean2012.783
Median Absolute Deviation (MAD)2
Skewness-0.68987871
Sum2012783
Variance10.278189
MonotonicityNot monotonic
2025-05-24T15:30:07.832342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2016 297
29.7%
2015 127
12.7%
2014 98
 
9.8%
2013 91
 
9.1%
2012 64
 
6.4%
2011 63
 
6.3%
2010 60
 
6.0%
2007 53
 
5.3%
2008 52
 
5.2%
2009 51
 
5.1%
ValueCountFrequency (%)
2006 44
 
4.4%
2007 53
5.3%
2008 52
5.2%
2009 51
5.1%
2010 60
6.0%
2011 63
6.3%
2012 64
6.4%
2013 91
9.1%
2014 98
9.8%
2015 127
12.7%
ValueCountFrequency (%)
2016 297
29.7%
2015 127
12.7%
2014 98
 
9.8%
2013 91
 
9.1%
2012 64
 
6.4%
2011 63
 
6.3%
2010 60
 
6.0%
2009 51
 
5.1%
2008 52
 
5.2%
2007 53
 
5.3%

Runtime
Real number (ℝ)

Distinct94
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.172
Minimum66
Maximum191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2025-05-24T15:30:08.169780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile88
Q1100
median111
Q3123
95-th percentile150
Maximum191
Range125
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.810908
Coefficient of variation (CV)0.16621521
Kurtosis0.8583211
Mean113.172
Median Absolute Deviation (MAD)12
Skewness0.84671273
Sum113172
Variance353.85027
MonotonicityNot monotonic
2025-05-24T15:30:08.520264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108 31
 
3.1%
100 28
 
2.8%
117 27
 
2.7%
118 26
 
2.6%
106 26
 
2.6%
110 26
 
2.6%
102 25
 
2.5%
112 24
 
2.4%
123 23
 
2.3%
104 23
 
2.3%
Other values (84) 741
74.1%
ValueCountFrequency (%)
66 1
 
0.1%
73 2
 
0.2%
80 2
 
0.2%
81 5
0.5%
82 1
 
0.1%
83 6
0.6%
84 3
 
0.3%
85 9
0.9%
86 8
0.8%
87 9
0.9%
ValueCountFrequency (%)
191 1
 
0.1%
187 1
 
0.1%
180 3
0.3%
172 1
 
0.1%
170 1
 
0.1%
169 3
0.3%
166 1
 
0.1%
165 5
0.5%
164 1
 
0.1%
163 1
 
0.1%

Rating
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7232
Minimum1.9
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-05-24T15:30:08.872923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile5.1
Q16.2
median6.8
Q37.4
95-th percentile8.1
Maximum9
Range7.1
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.94542879
Coefficient of variation (CV)0.14062185
Kurtosis1.3222703
Mean6.7232
Median Absolute Deviation (MAD)0.6
Skewness-0.74314194
Sum6723.2
Variance0.8938356
MonotonicityNot monotonic
2025-05-24T15:30:09.232697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.1 52
 
5.2%
6.7 48
 
4.8%
7 46
 
4.6%
6.3 44
 
4.4%
7.3 42
 
4.2%
7.2 42
 
4.2%
6.6 42
 
4.2%
6.5 40
 
4.0%
7.8 40
 
4.0%
6.2 37
 
3.7%
Other values (45) 567
56.7%
ValueCountFrequency (%)
1.9 1
 
0.1%
2.7 2
0.2%
3.2 1
 
0.1%
3.5 2
0.2%
3.7 2
0.2%
3.9 3
0.3%
4 1
 
0.1%
4.1 1
 
0.1%
4.2 2
0.2%
4.3 4
0.4%
ValueCountFrequency (%)
9 1
 
0.1%
8.8 2
 
0.2%
8.6 3
 
0.3%
8.5 6
 
0.6%
8.4 4
 
0.4%
8.3 7
 
0.7%
8.2 10
 
1.0%
8.1 26
2.6%
8 19
1.9%
7.9 23
2.3%

Votes
Real number (ℝ)

High correlation 

Distinct997
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169808.26
Minimum61
Maximum1791916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-05-24T15:30:09.591987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile1260.35
Q136309
median110799
Q3239909.75
95-th percentile526551.85
Maximum1791916
Range1791855
Interquartile range (IQR)203600.75

Descriptive statistics

Standard deviation188762.65
Coefficient of variation (CV)1.1116223
Kurtosis11.312681
Mean169808.26
Median Absolute Deviation (MAD)88402
Skewness2.5079185
Sum1.6980826 × 108
Variance3.5631337 × 1010
MonotonicityNot monotonic
2025-05-24T15:30:09.942834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97141 2
 
0.2%
291 2
 
0.2%
1427 2
 
0.2%
757074 1
 
0.1%
5796 1
 
0.1%
168875 1
 
0.1%
136323 1
 
0.1%
26320 1
 
0.1%
75291 1
 
0.1%
206707 1
 
0.1%
Other values (987) 987
98.7%
ValueCountFrequency (%)
61 1
0.1%
96 1
0.1%
102 1
0.1%
115 1
0.1%
164 1
0.1%
173 1
0.1%
178 1
0.1%
198 1
0.1%
202 1
0.1%
220 1
0.1%
ValueCountFrequency (%)
1791916 1
0.1%
1583625 1
0.1%
1222645 1
0.1%
1047747 1
0.1%
1045588 1
0.1%
1039115 1
0.1%
959065 1
0.1%
937414 1
0.1%
935408 1
0.1%
913152 1
0.1%

Revenue
Real number (ℝ)

High correlation 

Distinct815
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.48004
Minimum0
Maximum936.63
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-05-24T15:30:10.364072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.319
Q117.4425
median47.985
Q399.1775
95-th percentile281.2995
Maximum936.63
Range936.63
Interquartile range (IQR)81.735

Descriptive statistics

Standard deviation97.118097
Coefficient of variation (CV)1.2374879
Kurtosis12.66413
Mean78.48004
Median Absolute Deviation (MAD)35.68
Skewness2.8482239
Sum78480.04
Variance9431.9247
MonotonicityNot monotonic
2025-05-24T15:30:10.690981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.985 128
 
12.8%
0.03 7
 
0.7%
0.01 5
 
0.5%
0.02 4
 
0.4%
0.04 4
 
0.4%
0.05 4
 
0.4%
0.32 4
 
0.4%
0.54 3
 
0.3%
2.2 3
 
0.3%
1.29 3
 
0.3%
Other values (805) 835
83.5%
ValueCountFrequency (%)
0 1
 
0.1%
0.01 5
0.5%
0.02 4
0.4%
0.03 7
0.7%
0.04 4
0.4%
0.05 4
0.4%
0.06 2
 
0.2%
0.07 2
 
0.2%
0.08 1
 
0.1%
0.09 2
 
0.2%
ValueCountFrequency (%)
936.63 1
0.1%
760.51 1
0.1%
652.18 1
0.1%
623.28 1
0.1%
533.32 1
0.1%
532.17 1
0.1%
486.29 1
0.1%
458.99 1
0.1%
448.13 1
0.1%
424.65 1
0.1%

Metascore
Real number (ℝ)

High correlation 

Distinct85
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.985043
Minimum11
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-05-24T15:30:11.024291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile31
Q147.75
median58.985043
Q371
95-th percentile85
Maximum100
Range89
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation16.634858
Coefficient of variation (CV)0.28201825
Kurtosis-0.44853519
Mean58.985043
Median Absolute Deviation (MAD)11.985043
Skewness-0.12803962
Sum58985.043
Variance276.71851
MonotonicityNot monotonic
2025-05-24T15:30:11.358038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.98504274 64
 
6.4%
66 25
 
2.5%
68 25
 
2.5%
72 25
 
2.5%
64 24
 
2.4%
57 23
 
2.3%
65 22
 
2.2%
51 22
 
2.2%
76 21
 
2.1%
81 21
 
2.1%
Other values (75) 728
72.8%
ValueCountFrequency (%)
11 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
18 4
0.4%
19 1
 
0.1%
20 1
 
0.1%
22 3
0.3%
23 6
0.6%
24 2
 
0.2%
25 2
 
0.2%
ValueCountFrequency (%)
100 1
 
0.1%
99 1
 
0.1%
98 1
 
0.1%
96 4
0.4%
95 3
0.3%
94 3
0.3%
93 3
0.3%
92 2
 
0.2%
91 1
 
0.1%
90 5
0.5%

Genre_Count
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
1
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1000
100.0%

Length

2025-05-24T15:30:11.661043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T15:30:11.917159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1000
100.0%

Decade
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
2010s
800 
2000s
200 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5000
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010s
2nd row2010s
3rd row2010s
4th row2010s
5th row2010s

Common Values

ValueCountFrequency (%)
2010s 800
80.0%
2000s 200
 
20.0%

Length

2025-05-24T15:30:12.137405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-24T15:30:12.423971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2010s 800
80.0%
2000s 200
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 2200
44.0%
2 1000
20.0%
s 1000
20.0%
1 800
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4000
80.0%
Lowercase Letter 1000
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2200
55.0%
2 1000
25.0%
1 800
 
20.0%
Lowercase Letter
ValueCountFrequency (%)
s 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4000
80.0%
Latin 1000
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2200
55.0%
2 1000
25.0%
1 800
 
20.0%
Latin
ValueCountFrequency (%)
s 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2200
44.0%
2 1000
20.0%
s 1000
20.0%
1 800
 
16.0%

Interactions

2025-05-24T15:29:56.343233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:42.988262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:45.097264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:47.470286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:49.606261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:51.784221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:54.217410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:56.698363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:43.277609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:45.391781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:47.759772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:49.907475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:52.090222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:54.518686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:57.056436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:43.568652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:45.680804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:48.034888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:50.207122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:52.436544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:54.840565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:57.771650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:43.859816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:45.979572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:48.349130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:50.519764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:52.846050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:55.149417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:58.129858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:44.231212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:46.328245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:48.655595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:50.838284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:53.272432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:55.450183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:58.546169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:44.548978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:46.776414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:48.979041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:51.166176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:53.605722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:55.760524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:58.896285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:44.817506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:47.163835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:49.274949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:51.456865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:53.922463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2025-05-24T15:29:56.049592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2025-05-24T15:30:12.637254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
DecadeMetascoreRankRatingRevenueRuntimeVotesYear
Decade1.0000.0000.1440.1700.0970.1070.2300.996
Metascore0.0001.000-0.1860.6410.0490.1890.261-0.058
Rank0.144-0.1861.000-0.229-0.209-0.224-0.202-0.290
Rating0.1700.641-0.2291.0000.1360.3830.520-0.228
Revenue0.0970.049-0.2090.1361.0000.2130.591-0.217
Runtime0.1070.189-0.2240.3830.2131.0000.408-0.174
Votes0.2300.261-0.2020.5200.5910.4081.000-0.610
Year0.996-0.058-0.290-0.228-0.217-0.174-0.6101.000

Missing values

2025-05-24T15:29:59.329297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-24T15:30:00.034296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RankTitleGenreDescriptionDirectorActorsYearRuntimeRatingVotesRevenueMetascoreGenre_CountDecade
01Guardians of the Galaxy[Action,Adventure,Sci-Fi]A group of intergalactic criminals are forced to work together to stop a fanatical warrior from taking control of the universe.James Gunn[Chris Pratt, Vin Diesel, Bradley Cooper, Zoe Saldana]20141218.1757074333.13076.012010s
12Prometheus[Adventure,Mystery,Sci-Fi]Following clues to the origin of mankind, a team finds a structure on a distant moon, but they soon realize they are not alone.Ridley Scott[Noomi Rapace, Logan Marshall-Green, Michael Fassbender, Charlize Theron]20121247.0485820126.46065.012010s
23Split[Horror,Thriller]Three girls are kidnapped by a man with a diagnosed 23 distinct personalities. They must try to escape before the apparent emergence of a frightful new 24th.M. Night Shyamalan[James McAvoy, Anya Taylor-Joy, Haley Lu Richardson, Jessica Sula]20161177.3157606138.12062.012010s
34Sing[Animation,Comedy,Family]In a city of humanoid animals, a hustling theater impresario's attempt to save his theater with a singing competition becomes grander than he anticipates even as its finalists' find that their lives will never be the same.Christophe Lourdelet[Matthew McConaughey,Reese Witherspoon, Seth MacFarlane, Scarlett Johansson]20161087.260545270.32059.012010s
45Suicide Squad[Action,Adventure,Fantasy]A secret government agency recruits some of the most dangerous incarcerated super-villains to form a defensive task force. Their first mission: save the world from the apocalypse.David Ayer[Will Smith, Jared Leto, Margot Robbie, Viola Davis]20161236.2393727325.02040.012010s
56The Great Wall[Action,Adventure,Fantasy]European mercenaries searching for black powder become embroiled in the defense of the Great Wall of China against a horde of monstrous creatures.Yimou Zhang[Matt Damon, Tian Jing, Willem Dafoe, Andy Lau]20161036.15603645.13042.012010s
67La La Land[Comedy,Drama,Music]A jazz pianist falls for an aspiring actress in Los Angeles.Damien Chazelle[Ryan Gosling, Emma Stone, Rosemarie DeWitt, J.K. Simmons]20161288.3258682151.06093.012010s
78Mindhorn[Comedy]A has-been actor best known for playing the title character in the 1980s detective series "Mindhorn" must work with the police when a serial killer says that he will only speak with Detective Mindhorn, whom he believes to be a real person.Sean Foley[Essie Davis, Andrea Riseborough, Julian Barratt,Kenneth Branagh]2016896.4249047.98571.012010s
89The Lost City of Z[Action,Adventure,Biography]A true-life drama, centering on British explorer Col. Percival Fawcett, who disappeared while searching for a mysterious city in the Amazon in the 1920s.James Gray[Charlie Hunnam, Robert Pattinson, Sienna Miller, Tom Holland]20161417.171888.01078.012010s
910Passengers[Adventure,Drama,Romance]A spacecraft traveling to a distant colony planet and transporting thousands of people has a malfunction in its sleep chambers. As a result, two passengers are awakened 90 years early.Morten Tyldum[Jennifer Lawrence, Chris Pratt, Michael Sheen,Laurence Fishburne]20161167.0192177100.01041.012010s
RankTitleGenreDescriptionDirectorActorsYearRuntimeRatingVotesRevenueMetascoreGenre_CountDecade
990991Underworld: Rise of the Lycans[Action,Adventure,Fantasy]An origins story centered on the centuries-old feud between the race of aristocratic vampires and their onetime slaves, the Lycans.Patrick Tatopoulos[Rhona Mitra, Michael Sheen, Bill Nighy, Steven Mackintosh]2009926.612970845.80044.00000012000s
991992Taare Zameen Par[Drama,Family,Music]An eight-year-old boy is thought to be a lazy trouble-maker, until the new art teacher has the patience and compassion to discover the real problem behind his struggles in school.Aamir Khan[Darsheel Safary, Aamir Khan, Tanay Chheda, Sachet Engineer]20071658.51026971.20042.00000012000s
992993Take Me Home Tonight[Comedy,Drama,Romance]Four years after graduation, an awkward high school genius uses his sister's boyfriend's Labor Day party as the perfect opportunity to make his move on his high school crush.Michael Dowse[Topher Grace, Anna Faris, Dan Fogler, Teresa Palmer]2011976.3454196.92058.98504312010s
993994Resident Evil: Afterlife[Action,Adventure,Horror]While still out to destroy the evil Umbrella Corporation, Alice joins a group of survivors living in a prison surrounded by the infected who also want to relocate to the mysterious but supposedly unharmed safe haven known only as Arcadia.Paul W.S. Anderson[Milla Jovovich, Ali Larter, Wentworth Miller,Kim Coates]2010975.914090060.13037.00000012010s
994995Project X[Comedy]3 high school seniors throw a birthday party to make a name for themselves. As the night progresses, things spiral out of control as word of the party spreads.Nima Nourizadeh[Thomas Mann, Oliver Cooper, Jonathan Daniel Brown, Dax Flame]2012886.716408854.72048.00000012010s
995996Secret in Their Eyes[Crime,Drama,Mystery]A tight-knit team of rising investigators, along with their supervisor, is suddenly torn apart when they discover that one of their own teenage daughters has been brutally murdered.Billy Ray[Chiwetel Ejiofor, Nicole Kidman, Julia Roberts, Dean Norris]20151116.22758547.98545.00000012010s
996997Hostel: Part II[Horror]Three American college students studying abroad are lured to a Slovakian hostel, and discover the grim reality behind it.Eli Roth[Lauren German, Heather Matarazzo, Bijou Phillips, Roger Bart]2007945.57315217.54046.00000012000s
997998Step Up 2: The Streets[Drama,Music,Romance]Romantic sparks occur between two dance students from different backgrounds at the Maryland School of the Arts.Jon M. Chu[Robert Hoffman, Briana Evigan, Cassie Ventura, Adam G. Sevani]2008986.27069958.01050.00000012000s
998999Search Party[Adventure,Comedy]A pair of friends embark on a mission to reunite their pal with the woman he was going to marry.Scot Armstrong[Adam Pally, T.J. Miller, Thomas Middleditch,Shannon Woodward]2014935.6488147.98522.00000012010s
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